Wednesday 17 September 2025
Artificial Intelligence (AI) has made tremendous progress in recent years, but one of its biggest limitations is its inability to explain its decisions. This lack of transparency can be a major issue, especially when it comes to making critical decisions that affect people’s lives.
One popular approach to addressing this problem is called Local Interpretable Model-Agnostic Explanations (LIME). LIME creates an interpretable model that approximates the behavior of a complex AI system, allowing us to understand why it made a particular decision. However, LIME has its limitations. It relies on random perturbations and sampling, which can lead to locality and instability issues, especially when working with limited training data.
Researchers have been trying to improve LIME by introducing instance-based transfer learning into the framework. The idea is to leverage relevant real instances from a related source domain to aid the explanation process in the target domain. This approach has shown promising results, but there are still challenges to overcome.
A new study proposes a novel Instance-Based Transfer Learning LIME (ITL-LEME) framework that aims to enhance explanation fidelity and stability in data-constrained environments. The researchers employ clustering to partition the source domain into clusters with representative prototypes. Instead of generating random perturbations, ITL-LIME retrieves pertinent real source instances from the source cluster whose prototype is most similar to the target instance.
These source instances are then combined with the target instance’s neighboring real instances to create a weighted set. A contrastive learning-based encoder is used as a weighting mechanism to assign weights to the instances based on their proximity to the target instance. Finally, these weighted instances are used to train the surrogate model for explanation purposes.
The study demonstrates that ITL-LIME greatly improves the stability and fidelity of LIME explanations in scenarios with limited data. This is achieved by reducing the reliance on random perturbations and instead using real-world instances from a related source domain.
The implications of this research are significant. By providing more accurate and stable explanations, AI systems can become more transparent and trustworthy. This could lead to increased adoption of AI technology in high-stakes domains such as healthcare and finance, where decision-making accuracy is critical.
Moreover, the ITL-LIME framework has the potential to be applied to a wide range of AI applications, from image classification to natural language processing.
Cite this article: “Enhancing Explainability in Artificial Intelligence with Instance-Based Transfer Learning”, The Science Archive, 2025.
Artificial Intelligence, Local Interpretable Model-Agnostic Explanations, Transfer Learning, Instance-Based Transfer Learning, Itl-Lime, Lime, Explanation Fidelity, Stability, Data-Constrained Environments, Ai Transparency, Trust
